I am trying to compare the model parameters among three multivariate multiple regressions. All three models incorporate date from the same 97 individuals and share the same 4 independent variables (age, sex, age*sex, number of missing teeth), but each has a different set of dependent variables (3D landmark coordinates for 97 individuals for three different regions of the skeleton). I would like to know if, for example, age accounts for the same proportion of variance in regions 1, 2, and 3.

It was suggested to me that I should be using seemingly unrelated regressions (SUR) to fit all three models simultaneously using the R package "systemfit." However, the function only supports a single response variable (a vector as the dependent variable) and I need to be able to input a multivariate response variable (as a matrix).

So, I have two questions. (1) Is SUR the correct method to be pursuing to address my research questions? If not, can someone please suggest an alternative? (2) Does anyone have any suggestions about how I could perform a multivariate SUR in R?


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.